Efficiency of National Governance in Managing Long-Term Greenhouse Gas Emission Reduction in the Agricultural Sector Towards the Thailand 5.0 Goal
Abstract
:1. Introduction
- Security Strategy—Enhancing national security and stability.
- Competitiveness Strategy—Building competitive capabilities.
- Human Development Strategy—Fostering human potential and skills development.
- Social Equality Strategy—Promoting social equality and equity.
- Environmental Quality Growth Strategy—Advancing growth in harmony with environmental sustainability.
- Public Sector Management Strategy—Balancing and improving the efficiency of public administration.
2. Literature Review
- Selection of Indicators: The indicators in this research were selected based on the Augmented Dickey–Fuller approach. Secondary data from relevant agencies responsible for information management between 1992 and 2024 were utilized, ensuring consistency and suitability for this analysis. Indicators were categorized into four latent variables: economic, social, civil politics, and environmental. Each latent variable comprises 25 observed indicators, structured according to Thailand’s policy-driven index formulation in each domain. These indicators align with national policymaking and correspond to the research questions for each of the four latent variables, influencing the analysis as causal factors. For this study, secondary data were chosen, which consist of information from key sources responsible for greenhouse gas emissions in Thailand, including the Department of Alternative Energy Development and Efficiency, the Office of the National Economic and Social Development Council (NESDC), the Thailand Greenhouse Gas Management Organization (Public Organization), and the Ministry of Agriculture and Cooperatives.
- Analysis of Relationships: The influence of relationships was analyzed using the Path-SFIML Model. Any indicator that failed to meet the criteria was excluded from the model. Specifically, indicators that were not stationary at level I(0) or level I(1) were immediately removed, as their inclusion would disrupt the model’s ability to maintain white noise characteristics, ultimately affecting the accuracy of the analysis.
- Validation Check: The validity of the Path-SFIML Model was checked. If there was a lack of validity in some or all components, the process was restarted from step 1.
- Measurement of Model Fit: The model’s fit was assessed. If any criterion was not met, the model was revised, and the process was restarted from the beginning.
- Spuriousness Check: The study ensured that issues related to estimation results, such as heteroscedasticity, multicollinearity, and autocorrelation, did not occur. If any of these issues were detected, the model was revised and restarted immediately.
- Final Model: Once the best model was identified, the selected indicators were used to identify new urgent scenario policies through sensitivity analysis.
- Forecasting Greenhouse Gas Emissions: The study forecasted future greenhouse gas emissions within the framework of the country’s 20 Year National Strategy (2018–2037).
- Qualitative research was conducted by organizing focus groups with stakeholders to gather approaches for formulating long-term national strategies.
3. Material and Methods
3.1. Structure (Specification) of the Path-SFIML Model
- Measurement Model: This model specifies the linear relationships between latent variables and observed variables, consisting of: (1) a measurement model for the external latent variables, or a measurement model for the independent variables; and (2) a measurement model for the internal latent variables, or a measurement model for the dependent variables. The analysis process in the measurement model is similar to Confirmatory Factor Analysis (CFA) [52]. The analysis begins with component analysis of the set of observed variables for each latent variable only. In factor analysis, all observed variables have a factor loading in every component. However, in the measurement model, each observed variable has a loading only in the specific latent variable it measures.
- Structural Model: This model specifies the relationship between exogenous latent variables (independent variables) and endogenous latent variables (dependent variables). The specification of the structural model must be based on sound theory, because the analysis of this model is essentially a validation process, confirming whether the relationships observed from empirical data align with the constructed model.Moderated Mediation Model: The mediation model helps explain and expand upon how well the influence of the independent variable is transmitted to the dependent variable. The mediation model enhances the effectiveness of the relationship by increasing its explanatory power and ensuring the influence of the relationship is more realistic.
3.2. Testing the White Noise Property
3.3. Validity Testing of the Path-SFIML Model
- Discriminant Validity: This refers to , which is the value derived from the Average Heterotrait–Heteromethod/Geometric Mean of the Average Monotrait–Heteromethod Correlation of construct N and the Average Monotrait–Heteromethod Correlation of construct .
- Convergent Validity: In this model, convergent validity must meet the criteria, which include:, Cronbach’s alpha , composite reliability (CR) , and the absence of multicollinearity in the indicators, which can be measured by the variance inflation factor (VIF) , which are the additional criteria used to assess the convergent validity.
3.4. Measurement of Model Fit
- The Chi-square statistics must meet the criterion: .
- The Root Mean Squared Residual must meet the criterion: .
- The Root Mean Square Residual Error of Approximation must meet the criterion: .
- The Standard Root Mean Square Residual must meet the criterion: .
- The Normal Fit Index must meet the criterion: .
- The Non-Normed Fit Index must meet the criterion: .
- The Comparative Fit Index must meet the criterion: .
- The Goodness of Fit Index must meet the criterion: .
- The Adjusted Goodness of Fit Index must meet the criterion:
3.5. Performance Evaluation of the Path-SFIML Model
4. Empirical Analysis
4.1. Selection of Indicators in Each Sector
4.2. Analysis of Adaptability to Equilibrium Based on the Path-SFIML Model
4.3. Analysis of Relationship Influences Using the Path-SFIML Model
- Validity of the Path-SFIML Model: The validity checks revealed that , , Cronbach’s alpha, , and Composite Reliability (CR), . Based on the validity check results, the model meets the validity criteria and passes all checks.
- Measurement of Model Fit: The details are as follows:1. Chi-Square Statistics,2. Root Mean Squared Residual,3. Root Mean Square Residual Error of Approximation,4. Standard Root of Mean Square Residual,5. Normal Fit Index,6. Non-Normed Fit Index,7. Comparative Fit Index,8. Good of Fit Index,9. Adjusted Goodness of Fit Index,
- Spuriousness Check: The analysis revealed no issues with heteroscedasticity (), multicollinearity (), or autocorrelation (Durbin Watson: D.W. = 2.01).
4.4. Waste and Greenhouse Gas Emission Forecasting Model Using the Path-SFIML Model
4.5. Focus Group Meeting with Stakeholders
- Support mechanisms for biodiversity conservation, which include tree banks, green bonds, pilot measures for payment for ecosystem services (PES), environmental funds, and tax incentives for community forest projects that reduce global warming. Tax reductions should be provided to businesses involved in research, knowledge development, or activities promoting sustainable biodiversity conservation and utilization. Laws should be amended, and new laws enacted, to encourage participation in biodiversity management, such as the Promotion of Marine and Coastal Resources Management Act, B.E. 2558, Forest Plantation Act (Amendment No. 2), B.E. 2558, National Reserved Forest Act (Amendment No. 4), B.E. 2559, Fisheries Act (Amendment No. 2), B.E. 2560, National Environmental Quality Promotion and Protection Act (Amendment No. 2), B.E. 2561, Wildlife Preservation and Protection Act, B.E. 2562, National Parks Act, B.E. 2562, Community Forest Act, B.E. 2562, and Forest Act (Amendment No. 8), B.E. 2562. Additionally, a draft Biodiversity Act, B.E. ……… should be prepared, incorporating mechanisms for collaboration at various levels.
- In the agricultural sector, actions should include advancing sustainable agricultural systems in land reform areas, establishing mechanisms for provincial wetland management, appointing committees for integrated strategic planning of wetland areas, and ensuring community participation in wetland management under relevant legal frameworks.
- The government must establish primary agencies under the Ministry of Natural Resources and Environment, Ministry of Public Health, and Ministry of Agriculture and Cooperatives to oversee access to and benefit-sharing of genetic resources. Mechanisms and regulations must be developed or improved for granting permissions and ensuring benefit-sharing from the use of genetic resources and local knowledge. Regulations should include the Fisheries Act (Amendment No. 2), B.E. 2560, and National Parks Act, B.E. 2562. Subcommittees for technical expertise in biodiversity access and benefit-sharing should be appointed. Projects should be implemented to create processes and develop tools and mechanisms for accessing and sharing benefits from biological resources. Preparations should be made for pilot communities regarding regulations and mechanisms for accessing and sharing benefits from genetic resources. Mechanisms and regulations related to benefit-sharing in research communities should also be established. Economic measures and mechanisms should ensure that the financial returns from bio-based products are reinvested into the source ecosystems to support sustainable biodiversity conservation and utilization, such as developing a 20 year bio-economy development strategy.
- Establishing Guidelines for Climate Change Management and International Trade and Investment Law: International trade and investment law plays a crucial role in climate change issues. Global economic activities, which are supported and facilitated by international trade and investment systems, are significant sources of greenhouse gas emissions. Addressing climate change requires new trade measures. Several environmental measures aimed at tackling climate change impact international trade and, therefore, become issues under international trade law. These measures include not only direct trade-related measures, such as border carbon adjustments implemented by states to reduce potential competitive disadvantages for domestic industries subject to high-cost climate policies, but also broader environmental policies. These include carbon taxes, emissions trading schemes, energy efficiency standards, and subsidies for renewable energy, such as feed-in tariffs. Therefore, to promote the shared benefits of free trade among states, the World Trade Organization (WTO) system imposes several restrictions on states’ freedom to develop national policy measures, including those related to climate change. WTO rules include provisions regarding environmental protection, particularly under the General Agreement on Tariffs and Trade (GATT). A state may justify the use of climate-related trade-restrictive measures under one of the exceptions recognized in Article XX of GATT. This requires a two-step analysis: first, determining whether the measure falls under one of the specified exceptions; and second, assessing whether the measure is applied in compliance with the “chapeau” of Article XX. Under these provisions, a measure that violates GATT rules may still be upheld if it is justified for the protection of human, animal, or plant life or health (Article XX(b)) or if it relates to the conservation of exhaustible natural resources. If such measures are enforced alongside domestic production or consumption restrictions (Article XX(g)), the WTO Appellate Body in the Shrimp/Turtle case observed that the phrase “exhaustible natural resources” in Article XX(g) must be interpreted in light of contemporary environmental concerns. It seems reasonable to argue that the global climate itself constitutes an “exhaustible natural resource”, which would bring climate mitigation policies within the scope of Article XX(g), as such policies are related to the conservation of these resources. Beyond the WTO exceptions, ongoing negotiations aim to establish an Environmental Goods Agreement (EGA) to eliminate tariffs on a wide range of goods that positively impact the climate. This effort seeks to align climate protection measures with trade regulations. In any case, since the climate regime has left the resolution of tensions between trade and climate policy considerations to trade law, the role of trade law in setting standards and resolving disputes becomes crucial. Given that the Paris Agreement lacks provisions for a binding dispute resolution system, trade law’s mechanisms for standard-setting and dispute resolution will play a key role in shaping climate action strategies.
- The process of raising public awareness in resource management can be carried out in six steps: establishing public understanding, stimulating and creating motivation, persuading for acceptance, guiding for practical implementation, reinforcing continuity, and recognizing and disseminating achievements. This process fosters knowledge, understanding, and realization of responsible and efficient resource utilization, ensuring maximum benefit and sustainability in the future. Effective implementation must be carried out under a participatory approach, beginning with information awareness, followed by collaborative support, feedback collection, decision-making involvement, and citizen empowerment. The public prioritizes transparent and clear information that is free from distortion or bias, complete, factual, and trustworthy, as this influences their ability to make informed decisions. To strengthen participation in policy discussions, forums for open dialogue should be established where all stakeholders can express their views in an atmosphere of unity and mutual trust. Participants should share perspectives based on goodwill, cooperation, and fairness, ensuring discussions remain transparent and free from hidden agendas. Decision-makers should have adequate knowledge and a deep understanding of rational justifications to ensure timely and appropriate decisions. Raising awareness about natural resource and environmental conservation involves both theoretical knowledge and practical policy implementation, educating the public, especially farmers and local communities, about the significance of ecosystems, factors contributing to environmental degradation, and policies that encourage public participation and ownership of natural resources. By integrating education, participation, and policy implementation, sustainable resource management can be achieved, ensuring long-term environmental conservation and collective responsibility for future generations.
- The government must take urgent action to enhance the absorption of greenhouse gas emissions [60]. According to the carbon cycle, carbon dioxide (CO2) is stored or absorbed in three main sinks: oceans (Blue Carbon), forests (Green Carbon), and the atmosphere. However, the atmospheric carbon sink is problematic since it stores carbon in the form of CO2, a greenhouse gas that contributes to global warming. Therefore, to improve the efficiency of carbon storage and absorption, priority must be given to two main sinks: oceans (Blue Carbon) and forests (Green Carbon), which are detailed as follows:
- Blue Carbon: Blue Carbon refers to atmospheric carbon in the form of CO2 that is absorbed by marine ecosystems, particularly through key coastal and marine habitats such as mangrove forests, salt marshes, tidal wetlands, seagrasses, and large seaweeds. Among these, mangrove ecosystems play a crucial role in sequestering CO2 and storing it in underwater soil or coastal sediments. The significance of Blue Carbon includes:
- 1.1
- The ocean is the world’s largest carbon sink, known as the “Oceanic Carbon Sink”, which absorbs and stores more than a quarter of human-generated CO2 emissions.
- 1.2
- Although the biomass of marine plants is relatively small compared to terrestrial plants that store Green Carbon, marine vegetation has a higher carbon sequestration capacity and can store carbon for thousands of years.
- 1.3
- Some governments have prioritized addressing marine pollution, recognizing that an effective solution to greenhouse gas emissions should start with tackling ocean pollution on a global scale.
- Green Carbon: Green Carbon refers to atmospheric CO2 that is absorbed and stored by trees, forests, and land covered with vegetation through photosynthesis. This type of carbon sink is widely recognized as it stores excess CO2 in terrestrial ecosystems, earning it the name “Terrestrial Carbon Sink”. The significance of Green Carbon includes:
- 2.1
- Trees and forests serve as highly efficient Green Carbon absorbers due to their long lifespan. In particular, accumulated leaf litter and wood debris in forests store CO2 for centuries.
- 2.2
- Forests can rapidly sequester large amounts of CO2. Therefore, afforestation and reforestation efforts are effective means of enhancing carbon storage and absorption.
5. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Tau Test | ) | |
---|---|---|
Variable | First Difference I(1) Value | |
−4.05 *** | ||
−4.50 *** | ||
−4.24 *** | ||
−4.59 *** | ||
−4.65 *** | ||
−4.72 *** | ||
−4.49 *** | ||
−4.59 *** | ||
−5.41 *** | ||
−4.30 *** | ||
−4.45 *** | ||
−4.25 *** | ||
−4.55 *** | ||
−5.19 *** | ||
−4.55 *** | ||
−4.25 *** | ||
−4.79 *** | ||
−4.95 *** | ||
−5.01 *** | ||
−4.75 *** | ||
−5.11 *** | ||
−4.77 *** | ||
−5.05 *** | ||
−4.95 *** | ||
−4.90 *** |
Variables | Trace Statistic Test | Max—Eigen Statistic Test | MacKinnon Critical Value (p-Value) | |
---|---|---|---|---|
162.15 *** | 215.02 *** | −0.62 *** | p < 0.01 | |
−0.36 *** | ||||
−0.88 *** | ||||
−0.002 *** | ||||
Dependent Variables | Type of Effect | Independent Variables | |||
---|---|---|---|---|---|
Economic | Social | Environmental | Civil Politics | ||
Economic | DE | - | - | - | 0.89 *** |
IE | - | - | - | - | |
Social | DE | 0.36 *** | - | - | 0.85 *** |
IE | - | - | - | 0.12 *** | |
Environmental | DE | 0.54 *** | 0.25 *** | - | 0.81 *** |
IE | 0.09 *** | - | - | 0.19 *** | |
Civil politics | DE | - | - | - | - |
IE | - | - | - | - |
Forecasting Model | MAPE (%) | RMSE (%) |
---|---|---|
MRM model | 16.55 | 17.65 |
ANN model | 5.52 | 7.11 |
ANIF model | 5.49 | 7.05 |
GM (1,1) | 5.05 | 5.44 |
BG (1,1) | 2.99 | 3.09 |
NN model | 2.69 | 2.92 |
Convolutional Neural Network | 2.50 | 2.71 |
Path-GMM-Nearest-neighbor model | 1.11 | 1.23 |
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Sutthichaimethee, P.; Saraphirom, P.; Junsiri, C. Efficiency of National Governance in Managing Long-Term Greenhouse Gas Emission Reduction in the Agricultural Sector Towards the Thailand 5.0 Goal. Sustainability 2025, 17, 3959. https://doi.org/10.3390/su17093959
Sutthichaimethee P, Saraphirom P, Junsiri C. Efficiency of National Governance in Managing Long-Term Greenhouse Gas Emission Reduction in the Agricultural Sector Towards the Thailand 5.0 Goal. Sustainability. 2025; 17(9):3959. https://doi.org/10.3390/su17093959
Chicago/Turabian StyleSutthichaimethee, Pruethsan, Phayom Saraphirom, and Chaiyan Junsiri. 2025. "Efficiency of National Governance in Managing Long-Term Greenhouse Gas Emission Reduction in the Agricultural Sector Towards the Thailand 5.0 Goal" Sustainability 17, no. 9: 3959. https://doi.org/10.3390/su17093959
APA StyleSutthichaimethee, P., Saraphirom, P., & Junsiri, C. (2025). Efficiency of National Governance in Managing Long-Term Greenhouse Gas Emission Reduction in the Agricultural Sector Towards the Thailand 5.0 Goal. Sustainability, 17(9), 3959. https://doi.org/10.3390/su17093959